Densely Connected Residual Network for Attack Recognition
This addresses security challenges in threat detection for edge, fog, and cloud computing environments, though it appears incremental as it builds on existing residual network concepts.
The paper tackles the problem of high false alarm rates and low detection rates in unknown threat perception by proposing a densely connected residual network (Densely-ResNet) for attack recognition, which accurately discovers various unknown threats across edge, fog, and cloud layers while maintaining a much lower false alarm rate than existing algorithms.
High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected residual network (Densely-ResNet) for attack recognition. Densely-ResNet is built with several basic residual units, where each of them consists of a series of Conv-GRU subnets by wide connections. Our evaluation shows that Densely-ResNet can accurately discover various unknown threats that appear in edge, fog and cloud layers and simultaneously maintain a much lower false alarm rate than existing algorithms.